Hello all,
I am running xtlogit command in Stata 14.2 and my main variable of interest includes a quadratic term, which I included based on theory and a utest confirming the presence of a U-shape.
I am working with an unbalanced panel with 15,165 observations (see example data below). The panel variable is id_ocad and the time variable is semester
My concern is that when I run the command using the # operator to generate the quadratic, the coefficient on the quadratic term is reported as 0 and the standard error is omitted in the output table.
However, when I manually generate the quadratic term and include it in the (otherwise) identical regression, the coefficient is reported as statistically significant and non-zero, and a utest confirms the presence of a U-shape, as mentioned above.
I imagine there is a reason for the different outputs, which may tell me something important about my data and the appropriateness of the model I am running.
In addition, as I would like to use margins after estimation, I would need to use the # operator to generate the quadratic term if possible.
Thank you in advance for any suggestions.
Best regards,
Theo
I am running xtlogit command in Stata 14.2 and my main variable of interest includes a quadratic term, which I included based on theory and a utest confirming the presence of a U-shape.
I am working with an unbalanced panel with 15,165 observations (see example data below). The panel variable is id_ocad and the time variable is semester
My concern is that when I run the command using the # operator to generate the quadratic, the coefficient on the quadratic term is reported as 0 and the standard error is omitted in the output table.
Code:
xtlogit prob_project n_projects_cumlag ln_densidad_pob ln_poblacion l2.ln_indice_desempeno l2.ln_tasa_mort l2.ln_balance ln_regalias_efec_cap c.months_election##c.months_election i.semester, fe vce(oim)
Code:
. xtlogit prob_project n_projects_cumlag ln_densidad_pob ln_poblacion l2.ln_indice_desempeno l2.ln_tasa_mort l2.ln_balance ln_regalias_efec_cap c.months_election##c.months_election i.semester, fe vce(oim)
note: c.months_election#c.months_election omitted because of collinearity
note: 12.semester omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: 139 groups (622 obs) dropped because of all positive or
all negative outcomes.
Iteration 0: log likelihood = -2597.9842
Iteration 1: log likelihood = -2448.5756
Iteration 2: log likelihood = -2437.0854
Iteration 3: log likelihood = -2437.0664
Iteration 4: log likelihood = -2437.0664
Conditional fixed-effects logistic regression Number of obs = 6,652
Group variable: id_ocad Number of groups = 796
Obs per group:
min = 2
avg = 8.4
max = 10
LR chi2(16) = 1162.32
Log likelihood = -2437.0664 Prob > chi2 = 0.0000
-----------------------------------------------------------------------------------------------------
prob_project | Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
n_projects_cumlag | -.2028613 .0168779 -12.02 0.000 -.2359414 -.1697811
ln_densidad_pob | 11.82582 58.98681 0.20 0.841 -103.7862 127.4378
ln_poblacion | -7.168727 58.99419 -0.12 0.903 -122.7952 108.4578
|
ln_indice_desempeno |
L2. | .3498155 .18455 1.90 0.058 -.0118958 .7115267
|
ln_tasa_mort |
L2. | -.0167774 .0661643 -0.25 0.800 -.146457 .1129022
|
ln_balance |
L2. | -3.038936 2.949626 -1.03 0.303 -8.820098 2.742225
|
ln_regalias_efec_cap | .0736869 .0083789 8.79 0.000 .0572645 .0901094
months_election | -.3035517 .0288018 -10.54 0.000 -.3600022 -.2471011
|
c.months_election#c.months_election | 0 (omitted)
|
semester |
4 | -.5233661 .1549722 -3.38 0.001 -.827106 -.2196262
5 | -3.47403 .295229 -11.77 0.000 -4.052668 -2.895392
6 | -4.443102 .4478829 -9.92 0.000 -5.320936 -3.565268
7 | -6.573392 .6058894 -10.85 0.000 -7.760914 -5.385871
8 | -7.220639 .7680139 -9.40 0.000 -8.725919 -5.71536
9 | 3.111472 .491074 6.34 0.000 2.148984 4.073959
10 | 2.046095 .3227467 6.34 0.000 1.413523 2.678667
11 | .6660429 .1767568 3.77 0.000 .3196059 1.01248
12 | 0 (omitted)
-----------------------------------------------------------------------------------------------------
I imagine there is a reason for the different outputs, which may tell me something important about my data and the appropriateness of the model I am running.
In addition, as I would like to use margins after estimation, I would need to use the # operator to generate the quadratic term if possible.
Thank you in advance for any suggestions.
Best regards,
Theo
Code:
xtlogit prob_project n_projects_cumlag ln_densidad_pob ln_poblacion l2.ln_indice_desempeno l2.ln_tasa_mort l2.ln_balance ln_regalias_efec_cap months_election months_election_sq i.semester, fe vce(oim) utest months_election months_election_sq, prefix ( prob_project )
Code:
. xtlogit prob_project n_projects_cumlag ln_densidad_pob ln_poblacion l2.ln_indice_desempeno l2.ln_tasa_mort l2.ln_balance ln_regalias_efec_cap months_election months_election_sq i.semester, fe vce(oim)
note: 10.semester omitted because of collinearity
note: 12.semester omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: 139 groups (622 obs) dropped because of all positive or
all negative outcomes.
Iteration 0: log likelihood = -2597.9842
Iteration 1: log likelihood = -2448.5756
Iteration 2: log likelihood = -2437.0854
Iteration 3: log likelihood = -2437.0664
Iteration 4: log likelihood = -2437.0664
Conditional fixed-effects logistic regression Number of obs = 6,652
Group variable: id_ocad Number of groups = 796
Obs per group:
min = 2
avg = 8.4
max = 10
LR chi2(16) = 1162.32
Log likelihood = -2437.0664 Prob > chi2 = 0.0000
--------------------------------------------------------------------------------------
prob_project | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
n_projects_cumlag | -.2028613 .0168779 -12.02 0.000 -.2359414 -.1697811
ln_densidad_pob | 11.82582 58.98681 0.20 0.841 -103.7862 127.4378
ln_poblacion | -7.168727 58.99419 -0.12 0.903 -122.7952 108.4578
|
ln_indice_desempeno |
L2. | .3498155 .18455 1.90 0.058 -.0118958 .7115267
|
ln_tasa_mort |
L2. | -.0167774 .0661643 -0.25 0.800 -.146457 .1129022
|
ln_balance |
L2. | -3.038936 2.949626 -1.03 0.303 -8.820098 2.742225
|
ln_regalias_efec_cap | .0736869 .0083789 8.79 0.000 .0572645 .0901094
months_election | -1.838123 .2689011 -6.84 0.000 -2.365159 -1.311087
months_election_sq | .028418 .0044826 6.34 0.000 .0196323 .0372037
|
semester |
4 | -.5233661 .1549722 -3.38 0.001 -.827106 -.2196262
5 | -5.520125 .5898451 -9.36 0.000 -6.6762 -4.36405
6 | -10.58139 1.377183 -7.68 0.000 -13.28062 -7.882158
7 | -18.84996 2.491531 -7.57 0.000 -23.73327 -13.96665
8 | -27.68159 3.932863 -7.04 0.000 -35.38986 -19.97332
9 | -3.026814 .5554395 -5.45 0.000 -4.115455 -1.938172
10 | 0 (omitted)
11 | .6660429 .1767568 3.77 0.000 .3196059 1.01248
12 | 0 (omitted)
--------------------------------------------------------------------------------------
. utest months_election months_election_sq, prefix (prob_project)
(983 missing values generated)
(1,996 missing values generated)
Specification: f(x)=x^2
Extreme point: 32.34084
Test:
H1: U shape
vs. H0: Monotone or Inverse U shape
-------------------------------------------------
| Lower bound Upper bound
-----------------+-------------------------------
Interval | 0 42
Slope | -1.838123 .548988
t-value | -6.835684 5.063425
P>|t| | 4.44e-12 2.11e-07
-------------------------------------------------
Overall test of presence of a U shape:
t-value = 5.06
P>|t| = 2.11e-07
Code:
input float(prob_project n_projects_cumlag ln_densidad_pob ln_poblacion ln_indice_desempeno ln_tasa_mort ln_balance ln_regalias_efec_cap months_election months_election_sq semester) long id_ocad 0 0 3.945458 9.845434 4.21763 2.961141 13.6579 11.47631 42 1764 1 0 0 0 3.945458 9.845434 4.21763 2.961141 13.6579 11.47631 36 1296 2 0 0 0 3.9661324 9.865941 4.2298265 2.947067 13.654828 10.629907 30 900 3 0 0 0 3.9661324 9.865941 4.2298265 2.947067 13.654828 10.629907 24 576 4 0 1 0 3.9862025 9.886138 4.3641763 3.884652 13.655166 12.31328 18 324 5 0 0 1 3.9862025 9.886138 4.3641763 3.884652 13.655166 12.31328 12 144 6 0 0 1 4.0066056 9.906583 4.0745883 1.541159 13.651732 12.624626 6 36 7 0 0 1 4.0066056 9.906583 4.0745883 1.541159 13.651732 12.624626 0 0 8 0 0 1 4.027492 9.927351 4.1196294 3.016025 13.637353 11.8977 42 1764 9 0 0 1 4.027492 9.927351 4.1196294 3.016025 13.637353 11.8977 36 1296 10 0 0 1 4.047253 9.947169 4.064282 . 13.651488 11.55211 30 900 11 0 0 1 4.047253 9.947169 4.064282 . 13.651488 11.55211 24 576 12 0 0 1 4.067316 9.96726 . . . . 18 324 13 0 0 1 4.067316 9.96726 . . . . 12 144 14 0 0 0 3.902377 12.139313 3.984617 2.933325 13.65175 11.759857 42 1764 1 60092 1 0 3.902377 12.139313 3.984617 2.933325 13.65175 11.759857 36 1296 2 60092 1 17 3.924149 12.16117 4.34484 3.034472 13.619888 11.960607 30 900 3 60092 1 20 3.924149 12.16117 4.34484 3.034472 13.619888 11.960607 24 576 4 60092 1 42 3.946038 12.1829 3.397157 2.9343886 13.68308 11.899978 18 324 5 60092 1 57 3.946038 12.1829 3.397157 2.9343886 13.68308 11.899978 12 144 6 60092 0 89 3.967458 12.204366 4.2517734 2.933325 13.644894 7.416076 6 36 7 60092 1 89 3.967458 12.204366 4.2517734 2.933325 13.644894 7.416076 0 0 8 60092 0 94 3.988799 12.225733 4.3862324 2.8673306 13.640287 11.284286 42 1764 9 60092 1 94 3.988799 12.225733 4.3862324 2.8673306 13.640287 11.284286 36 1296 10 60092 0 104 4.0098753 12.24682 4.229876 . 13.642162 11.316903 30 900 11 60092 1 104 4.0098753 12.24682 4.229876 . 13.642162 11.316903 24 576 12 60092 1 105 4.0306945 12.2676 . . . . 18 324 13 60092 1 109 4.0306945 12.2676 . . . . 12 144 14 60092 0 0 4.325456 9.145802 3.890944 2.3702438 13.65227 12.265366 42 1764 1 60093 1 0 4.325456 9.145802 3.890944 2.3702438 13.65227 12.265366 36 1296 2 60093 1 6 4.3317857 9.152076 3.598994 3.3991954 13.653942 13.31222 30 900 3 60093 1 7 4.3317857 9.152076 3.598994 3.3991954 13.653942 13.31222 24 576 4 60093 0 11 4.338989 9.159258 4.244644 2.519308 13.654224 12.26798 18 324 5 60093 1 11 4.338989 9.159258 4.244644 2.519308 13.654224 12.26798 12 144 6 60093 1 14 4.344195 9.164506 4.115339 . 13.645218 12.139977 6 36 7 60093 1 17 4.344195 9.164506 4.115339 . 13.645218 12.139977 0 0 8 60093 0 18 4.351052 9.1713915 4.0765953 3.8811514 13.65453 11.402854 42 1764 9 60093 1 18 4.351052 9.1713915 4.0765953 3.8811514 13.65453 11.402854 36 1296 10 60093 0 21 4.3574777 9.177817 4.1624994 . 13.653556 11.16387 30 900 11 60093 1 21 4.3574777 9.177817 4.1624994 . 13.653556 11.16387 24 576 12 60093 1 23 4.364372 9.184612 . . . . 18 324 13 60093 1 24 4.364372 9.184612 . . . . 12 144 14 60093 0 0 4.148517 10.408164 4.179895 1.978239 13.654896 10.157875 42 1764 1 60094 1 0 4.148517 10.408164 4.179895 1.978239 13.654896 10.157875 36 1296 2 60094 0 2 4.14091 10.40053 4.229979 2.0399208 13.653278 11.41471 30 900 3 60094 1 2 4.14091 10.40053 4.229979 2.0399208 13.653278 11.41471 24 576 4 60094 0 5 4.133405 10.392926 4.19092 3.098289 13.652154 10.42813 18 324 5 60094 1 5 4.133405 10.392926 4.19092 3.098289 13.652154 10.42813 12 144 6 60094 1 6 4.12552 10.38508 4.190453 3.221672 13.651053 10.259785 6 36 7 60094 1 8 4.12552 10.38508 4.190453 3.221672 13.651053 10.259785 0 0 8 60094 0 10 4.1174097 10.377016 4.222297 3.3697066 13.654318 -1.7917595 42 1764 9 60094 0 10 4.1174097 10.377016 4.222297 3.3697066 13.654318 -1.7917595 36 1296 10 60094 0 10 4.1097255 10.369295 4.272544 . 13.65218 10.17851 30 900 11 60094 1 10 4.1097255 10.369295 4.272544 . 13.65218 10.17851 24 576 12 60094 0 11 4.1014857 10.36107 . . . . 18 324 13 60094 1 11 4.1014857 10.36107 . . . . 12 144 14 60094 end
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